Systems and Methods for Machine Vision Based Object Recognition

1. A computer-implemented method, comprising: receiving at a processing system, a plurality of images corresponding to a user, wherein the plurality of images are associated with a plurality of cameras, and wherein the plurality of images represent the user; using the plurality of images and the processing system to identify the user; detecting an item selected by the user, wherein detection is done by the processing system and throughout a facility; generating a visual model of the item, wherein the visual model is generated using the processing system; using the processing system to associate one or more instances of the visual model and one or more corresponding spatial coordinates with one or more of the plurality of cameras throughout the facility; tracking movement of the item throughout the facility, wherein tracking is based on instances of the visual model and corresponding spatial coordinates associated with the one or more of the plurality of cameras; determining whether the item is selected for purchase, wherein the determination is based on the tracked movement, and wherein the determination is made by the processing system; detecting that the user is leaving the facility, wherein the detection is performed by the processing system; and processing a transaction for the item when the item is selected for purchase and when the user has left the facility, wherein processing of the transaction is done by the processing system.

2. The computer-implemented method of claim 1 , further comprising: associating the item detected with the user.

3. The computer-implemented method of claim 2 , wherein associating the item detected with the user comprises: capturing an image of the user at a time of selecting the item; and comparing the image with one of the plurality of images of the user to identify the user and associating the item with the user.

4. The computer-implemented method of claim 3 , wherein the comparing is performed using a deep neural network trained to identify users using a machine learning algorithm.

5. The computer-implemented method of claim 1 , wherein the item is detected using a deep neural network trained to identify and label the item.

6. The computer-implemented method of claim 1 , wherein the visual model is a 2-dimensional representation of the item.

7. The computer-implemented method of claim 6 , wherein tracking the item is based on the 2-dimensional representation of the item.

8. The computer-implemented method of claim 1 , wherein the instances of the visual model of the item are 2-dimensional representations of the item.

9. The computer-implemented method of claim 1 , wherein detecting that the user is leaving the facility comprises: detecting the user in proximity of an entrance of the facility at a first time that is after a second time at which the plurality of images of the user are captured.

10. The computer-implemented method of claim 1 , wherein processing the transaction includes a cardless payment transaction and no financial information are exchanged between the user and an operator of the facility.

11. A processing system comprising: memory having computer-readable instructions stored therein; and one or more processors configured to execute the computer-readable instructions to: receive a plurality of images corresponding to a user, each of the plurality of images are associated with a plurality of cameras, and wherein the plurality of images represent the user; identify, using the plurality of images, the user; detect, throughout a facility, an item selected by the user; generate a visual model of the item; associate one or more instances of the visual model and one or more spatial coordinates with one or more of the plurality of cameras; track movement of the item throughout the facility based on instances of the visual model and the corresponding spatial coordinates associated with the one or more of the plurality of cameras; determine whether the item is selected for purchase based on the tracked movement; detect that the user is leaving the facility; and process a transaction for the item when the item is selected for purchase and when the user has left the facility.

12. The processing system of claim 11 , wherein the one or more processors are further configured to execute the computer-readable instructions to: associate the item detected with the user.

13. The processing system of claim 12 , wherein the one or more processors are configured to execute the computer-readable instructions to associate the item detected with the user by: capturing an image of the user at a time of selecting the item; and comparing the image with one of the plurality of images of the user to identify the user and associating the item with the user.

14. The processing system of claim 13 , wherein the one or more processors are configured to execute the computer-readable instructions to compare the image with one of the plurality of images of the user using a deep neural network trained to identify users using a machine learning algorithm.

15. The processing system of claim 11 , wherein the one or more processors are configured to execute the computer-readable instructions to detect the item using a deep neural network trained to identify and label the item.

16. The processing system of claim 11 , wherein the visual model is a 2-dimensional representation of the item.

17. The processing system of claim 16 , wherein the one or more processors are configured to execute the computer-readable instructions to track the item based on the 2-dimensional representation of the item.

18. The processing system of claim 11 , wherein the instances of the visual model of the item are 2-dimensional representations of the item.

19. The processing system method of claim 11 , wherein the one or more processors are configured to execute the computer-readable instructions to: detect that the user is leaving the facility by detecting the user in proximity of an entrance of the facility at a first time that is after a second time at which the plurality of images of the user are captured.

20. The processing system method of claim 11 , wherein processing the transaction includes a cardless payment transaction and no financial information are exchanged between the user and an operator of the facility.

21. One or more non-transitory computer-readable media comprising computer-readable instructions, which when executed by one or more processors, cause the one or more processors to: receive a plurality of images corresponding to a user, each of the plurality of images are associated with a plurality of cameras, and wherein the plurality of images represent the user; identify, using the plurality of images, the user; detect, throughout a facility, an item selected by the user; generate a visual model of the item; associate one or more instances of the visual model and one or more spatial coordinates with one or more of the plurality of cameras; track movement of the item throughout the facility based on instances of the visual model and the corresponding spatial coordinates associated with the one or more of the plurality of cameras; determine whether the item is selected for purchase based on the tracked movement; detect that the user is leaving the facility; and process a transaction for the item when the item is selected for purchase and when the user has left the facility.

22. The one or more non-transitory computer-readable media of claim 21 , wherein the execution of the computer-readable instructions by the one or more processors further cause the one or more processors to: associate the item detected with the user.

23. The one or more non-transitory computer-readable media of claim 22 , wherein the execution of the computer-readable instructions by the one or more processors further cause the one or more processors to associate the item detected with the user by: capturing an image of the user at a time of selecting the item; and comparing the image with one of the plurality of images of the user to identify the user and associating the item with the user.

24. The one or more non-transitory computer-readable media of claim 23 , wherein the execution of the computer-readable instructions by the one or more processors further cause the one or more processors to compare the image with one of the plurality of images of the user using a deep neural network trained to identify users using a machine learning algorithm.

25. The one or more non-transitory computer-readable media of claim 21 , wherein the execution of the computer-readable instructions by the one or more processors further cause the one or more processors to detect the item using a deep neural network trained to identify and label the item.

26. The one or more non-transitory computer-readable media of claim 21 , wherein the visual model is a 2-dimensional representation of the item.

27. The one or more non-transitory computer-readable media of claim 26 , wherein the execution of the computer-readable instructions by the one or more processors further cause the one or more processors to track the item based on the 2-dimensional representation of the item.

28. The one or more non-transitory computer-readable media of claim 21 , wherein the instances of the visual model of the item are 2-dimensional representations of the item.

29. The one or more non-transitory computer-readable media of claim 21 , wherein the execution of the computer-readable instructions by the one or more processors further cause the one or more processors to detect that the user is leaving the facility by detecting the user in proximity of an entrance of the facility at a first time that is after a second time at which the plurality of images of the user are captured.

30. The one or more non-transitory computer-readable media of claim 21 , wherein processing the transaction includes a cardless payment transaction and no financial information are exchanged between the user and an operator of the facility.